Global abundance patterns, diversity, and ecology of Patescibacteria in wastewater treatment plants

Background Microorganisms are responsible for nutrient removal and resource recovery in wastewater treatment plants (WWTPs), and their diversity is often studied by 16S rRNA gene amplicon sequencing. However, this approach underestimates the abundance and diversity of Patescibacteria due to the low coverage of commonly used PCR primers for this highly divergent bacterial phylum. Therefore, our current understanding of the global diversity, distribution, and ecological role of Patescibacteria in WWTPs is very incomplete. This is particularly relevant as Patescibacteria are considered to be associated with microbial host cells and can therefore influence the abundance and temporal variability of other microbial groups that are important for WWTP functioning. Results Here, we evaluated the in silico coverage of widely used 16S rRNA gene-targeted primer pairs and redesigned a primer pair targeting the V4 region of bacterial and archaeal 16S rRNA genes to expand its coverage for Patescibacteria. We then experimentally evaluated and compared the performance of the original and modified V4-targeted primers on 565 WWTP samples from the MiDAS global sample collection. Using the modified primer pair, the percentage of ASVs classified as Patescibacteria increased from 5.9 to 23.8%, and the number of detected patescibacterial genera increased from 560 to 1576, while the detected diversity of the remaining microbial community remained similar. Due to this significantly improved coverage of Patescibacteria, we identified 23 core genera of Patescibacteria in WWTPs and described the global distribution pattern of these unusual microbes in these systems. Finally, correlation network analysis revealed potential host organisms that might be associated with Patescibacteria in WWTPs. Interestingly, strong indications were found for an association between Patescibacteria of the Saccharimonadia and globally abundant polyphosphate-accumulating organisms of the genus Ca. Phosphoribacter. Conclusions Our study (i) provides an improved 16S rRNA gene V4 region-targeted amplicon primer pair inclusive of Patescibacteria with little impact on the detection of other taxa, (ii) reveals the diversity and distribution patterns of Patescibacteria in WWTPs on a global scale, and (iii) provides new insights into the ecological role and potential hosts of Patescibacteria in WWTPs﻿. Video Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s40168-024-01769-1.

Table S6.Primer sequences for the modified V4 primer pair compared with the original V4 primer pair Table S7.Metadata for wastewater treatment plants.
Table S8.Genus level richness comparisons between the data sets obtained by the original and the modified primer pairs.The slope of the observed ASV and Shannon index were predicted by the linear regression analysis using the modified and original primer sets.
Table S9.Blast mapping of patescibacterial ASVs to Danish WWTP metagenomic datasets.Three 100% identity mapping ASVs with >330 base pair coverage were highlighted in yellow.
Table S10.Global distribution of the patescibacterial genera identified as core community members from each country (average relative abundance).Table S11.Core patescibacterial genera identified if all four main process types of activated sludge samples were analysed together and MAG representation of core genera.
Table S12.Core patescibacterial genera identified in activated sludge samples from plants with carbon removal (C).
Table S13.Core patescibacterial genera identified in activated sludge samples from plants with carbon removal with nitrification (C, N).
Table S14.Core patescibacterial genera identified in activated sludge samples from plants with carbon removal, nitrification and denitrification (C, N, DN).
Table S15.Core patescibacterial genera identified in activated sludge samples from plants with carbon removal, nitrogen removal, enhanced biological phosphorus removal (C, N, DN, P / EBPR).
Table S16.List of patescibacterial genera that were characterized as CRAT genera Table S17.Correlated genus pairs (X and Y) with p value <0.001 and an absolute correlation value >0.5 identified by SparCC network analysis.Table S18.Correlated genus pairs (X and Y) with p value <0.001 and an absolute correlation value >0.5 identified by CoNet network analysis.Table S19.Correlated genus pairs (X and Y) with an absolute weight edge >0.2 identified by SPIEC-EASI network analysis.Table S20.Correlated ASV pairs (X and Y) with p value <0.001 and an absolute correlation value >0.5 identified by SparCC network analysis.Table S21.Correlated ASV pairs (X and Y) with p value <0.001 and an absolute correlation value >0.5 identified by CoNet network analysis.Table S22.Correlated ASV pairs (X and Y) with an absolute weight edge >0.2 identified by SPIEC-EASI network analysis.

Supplementary Figure 3 .
Phylum level richness comparison between the original and the modified primer pair.Panel a: The lollipop plot shows for different phyla the predicted ASV richness slope by a linear regression analysis between the ASV richness revealed in the overall 16S rRNA gene amplicon data set by the original primer pair and the modified primer pair.A slope of 1 means equal representation of a phylum by both primer pairs, a slope >1 higher representation, and a slope <1 lower representation by the modified primers.Only phyla with >0.1% average relative abundance in activated sludge samples are shown.Panel b-e: Regression analysis of ASV richness of Chloroflexi, Synergistota, Euryachaeota and Desulfobacterota obtained by the modified primer pair and the original primer pair of each sample after removing reads classified as Patescibacteria.Coefficient, R 2 value and p value are indicated in the title of each panel.The observed regression line is depicted in blue, the theoretical 1:1 line is indicated as a dashed line.Supplementary Figure 4. Prevalence of novel patescibacterial and nonpatescibacterial ASVs.Prevalence of patescibacterial and non-patescibacterial ASVs without high identity (99%) hits (a-d) and without genus level identity (94.5%) hits (e-h) against the MiDAS 4.8.1 and the SILVA r138.1 databases.Y-axis indicates the number of samples where each ASV was detected by the original primer pair and the modified primer pair.Supplementary Figure 5. Taxonomic affiliation of unclassified ASVs.Number of genus level unclassified ASVs in each class of Patescibacteria (Y-axis).The stacked barplot colours indicate for each Patescibacteria class the number of retrieved ASVs at their annotated taxonomic levels using the assignTaxonomy function in DADA2 against the MiDAS 4.8.1 database.